Creating the Water Chemistry Dataframe
1. Setting the Working Directory
1.0 Look for all files inside the Water Chemistry folder of the ā06. MOATs replication verificationā folder
#*********************************
## 1.) Set working directory
#*********************************
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir =
"/Users/katherinerovinski/GIT/NWFSC.MUK_KRL_SMR2019_waterchemistry")1.1 Initial Working Directoy
Inside the working directory (folder) is 1 CSV files generated after combining 13CSV logs representing 24second observations. These CSV files were created using the moats graph app
1.1.2 Link to Generating App
Link to moats.Graph.app Moving average window (nObs) set to 4.
1.1.3 Protocol with app
Every 17th observations was selected, the observations were 24second apart.
1.1.4 App data for 13 MOATS
1 CSVs per moats: M01, M02, M03, M04, M05, M06, M07, M08, M09, M10, M11, M12, M13. Files are also available on the OA Google drive. OA Drive for MOATS data
1.2 Subsampling
For speed and ease of computations the dataframe was extermely subsampled. Instead of the standard ān17ā used in the aquarium temperature investigation, this document subsampled at ān333ā.
1.2.1 Subsampling explained by observations
All observations (per the 13 MOATS logs) [1] 1137880 7 (6 seconds) Number of observations after subsampling every 17th response/observation [1] 66934 7 (24 seconds) Number of observations after the super-subsampling, every 333rd observation [1] 3417 7 (~30minutes) ***this wonāt detail the ramp
1.2.2 Subsampling Determination
To examine the Day to Night ramp (D2Nramp) researcher used n17 subsampling group
2.0) Calling and Reading in dml
#*********************************
## 2. Calling & Reading in " dml "
#*********************************
# Super Speedy subsampled data
dml <- read.csv(file = "M01thruM13moatslog_n17.csv", stringsAsFactors = FALSE)
dim(dml)## [1] 66934 72.2 In Situ measurements
#*********************************
## 2.2 Brining in In-Situ
#*********************************
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir =
"/Users/katherinerovinski/GIT/NWFSC.MUK_KRL_SMR2019_waterchemistry")
d.temp.insitu <- read.csv(file = "KRL19_insitu_sample_aTemperature_Day.csv", stringsAsFactors = FALSE)
n.temp.insitu <- read.csv(file = "KRL19_insitu_sample_aTemperature_Night.csv", stringsAsFactors = FALSE)
d.DO.insitu <- read.csv(file = "DO.PreSens.Krill.Day.csv", stringsAsFactors = FALSE)
n.DO.insitu <- read.csv(file = "DO.PreSens.Krill.Night.csv", stringsAsFactors = FALSE)
dn.DO.insitu <- read.csv(file = "DO.PreSens.Krill.DayNight.csv", stringsAsFactors = FALSE)
spec.insitu <- read.csv(file = "2020.10.26a_SpecData.csv", stringsAsFactors = FALSE)3.0) Reformatting variables/vectors (Factors)
Changing MOATs to Factors for the 13 different MOATs- these will be the discrete units for follow analysis
#*********************************
## 3. Reformatting variables/vectors (Factors)
#*********************************
## 3.3 Changing variables |
dml$moats <- factor(dml$moats)
# Checking the names of the different levels
levels(dml$moats)## [1] "M01" "M02" "M03" "M04" "M05" "M06" "M07" "M08" "M09" "M10" "M11" "M12"
## [13] "M13"## [1] 66934 74.0) Creating dateTime objects
Changes to the format of dates and times for observations
#*********************************
## 4.) Creating dateTime objects
#*********************************
# 4.0 establish the date time object of the CSV |
dml$dateTime <- as.POSIXct(dml$dateTime, format="%Y-%m-%d %H:%M:%OS")
ReferenceTime <- as.POSIXct("2019-09-20 23:59:00")
class(ReferenceTime)## [1] "POSIXct" "POSIXt"## [1] 66934 75.0) Creating Treatment Variables
5.1 Three Treatments
Three treatments of this studies are identified as * ācurrentā for the Current Conditions Treatment * āhightemperatureā for the High Temperature Conditions Treatment * āallchangeā for the All Change Conditions Treatment which incorporated both the high temperature conditions of the āhightemperatureā treatment along with exposure to lower aquarium pH.
#*********************************
## 5.) Creating Treatment Variables
#*********************************
## 5.1 Identifying treatments by moats
## establishing treatments
dml$treatment <- ""
dml$treatment[dml$moats == "M07" | dml$moats== "M10" | dml$moats== "M12"] <- "current"
dml$treatment[dml$moats == "M01"| dml$moats== "M06"] <- "hightemperature"
dml$treatment[dml$moats == "M02"| dml$moats== "M08" | dml$moats== "M13"] <- "allchange"
dml$treatment[dml$moats == "M03"| dml$moats == "M04" | dml$moats == "M05" | dml$moats== "M11"] <- "broken_and_ambientbroken"
#verify that this new column has been created
names(dml)## [1] "moats" "dateTime" "aTemperature" "sTemperature" "pH"
## [6] "DO" "salinity" "treatment"#results should include:
#[1] "moats" "dateTime" "aTemperature" "sTemperature" "pH"
#[6] "DO" "salinity" "treatment"
# QA check
dim(dml)## [1] 66934 85.1.1 Ambient Treatment
Those MOATS aquarium systems that did not reach desired/programed conditions were deemed the āambientā treatment. No temperature, DO, or pH conditions were programmed or alarmed. Flow control was governed by a solenoid valve to acheive flow/no-flow, day/night periods.
MOATS inside the āAmbientā treatment included MOATs 04 and MOATs 05 both were shown to have faulty thermisters. Recorded conditions of what the animals experienced canāt be guaranteed/confirmed. Without a record of aquarium settings MOATs 04 and MOAT 05 data was not included.
6.) Creating Night and Day Periods
6.1 Narrative (Overall)
Creating a day and night variables Day and night periods will only refer to time under treatment as a way to exclude the acclimation period. Day and night changed at about ~ 1230 on 05OCT19 Treatment start date considered to begin Monday 23SEP19 at 1200pm Krill Night Starts 1200 (~1230) and ends 2100 Krill Days Starts 2101 and ends 1159 (~1229) Interval 1 start 1200 23SEP19, end 1229 05OCT19 Interval 2 start 1230 05OCT19, end 2100 30OCT19
6.2 New Column, New Variable in dml
6.3 Disassembling dateTime to create 2 new variables
6.4 Narrative about Intervals
Interval 1 Interval Date Start ā2019-09-23ā Interval Date End ā2019-10-05ā Day Start Time ā21:01:00ā Day End Time ā11:29:00ā Night Start Time ā12:00:00ā Night End Time ā21:00:00ā DaytoNight Ramp(D2N) ā11:30:00ā start DaytoNight Ramp(D2N) ā11:59:00ā stop Other Time
Interval 2 Interval Date Start ā2019-10-05ā Interval Date End ā2019-10-30ā Day Start Time ā21:01:00ā Day End Time ā12:29:00ā Night Start Time ā12:30:00ā Night End Time ā21:00:00ā Other Time
6.5 Period Assignments
## 6.5 Day / Night Assignments
# Using the "case_when" function in the tidyverse in the place of a loop
dml <- dml %>% mutate(period=case_when(
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& (ObservationTime >= "12:00:00")
& (ObservationTime <="21:00:00") ~"night",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& (ObservationTime >= "12:31:00")
& (ObservationTime <="21:00:00") ~"night",
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& ((ObservationTime >= "21:01:00")
| (ObservationTime <="11:29:00")) ~"day",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& ((ObservationTime >= "21:01:00")
| (ObservationTime <= "12:01:00")) ~"day",
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& ((ObservationTime >= "11:30:00")
| (ObservationTime <="12:00:00")) ~"D2Nramp",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& ((ObservationTime >= "12:01:00")
| (ObservationTime <= "12:30:00")) ~"D2Nramp",
TRUE ~"other"
)
) 6.5.1 Quick Check on Period Generation
#Quick check to see if periods were created
period.intervals <- ggplot(dml, aes(x=dateTime, y=aTemperature)) +
geom_point(aes(colour=period, point=)) +
ggtitle("aTemperature Time Series Plots to Invesitgate Different Intervals") +
ylim (5.0, 15.00)
period.intervals## Warning: Removed 1471 rows containing missing values (geom_point).
6.5.1 Quick Check on Day to Night Ramp Verification
The ramp verification will be important to dissolved oxygen verification Dissolved oxygen not yet shown. Recorded dissolved oxygen needs to be corrected with observed salinity. The following 2 plots are only present to visualize the different periods across treatments
6.5.1a Quick Check on Day to Night Ramp Verification
ggplot(subset(dml,
period %in% ("D2Nramp")),
aes(x=dateTime, y=aTemperature)) +
geom_point(aes(colour=treatment, point=)) +
ylim (5.0, 15.00) +
ggtitle("aTemperature Values, All MOATs, During Day to Night Ramp Period")## Warning: Removed 26 rows containing missing values (geom_point).
7.) Creating different dataframes
7.0 Cdml āCleanedā dml dataframe is Cdml
Cdml <- dml %>% filter(!moats %in% c("M03", "M04", "M05", "M11")) %>%
filter(aTemperature>= 5 & aTemperature<=30) %>%
filter(treatment %in% c("current", "allchange", "hightemperature")) %>%
filter(period != "other")Creating the Water Chemistry Dataframe. Differences between dataframes outlined in the graphic below: 
#### Water Chemistry Dataframe Created āH2Ochemdfā
7.1 Removing the Acclimation Period on H2Ochemdf
The acclimation and the time after animals were removed from the system was designated other
8.0) Creating Corrected Salinity Value
8.1) Narrative: Creating Corrected Salinity Value from Point Measurments
Notes from the 2020.05.29 office hours creating 4 new variables in Water Chemistry Dataframe create a vector 181845 for the large n17 file create a vector 9283 observations long for the sub-sub sample n333 file logical vectors- boolean answers to those three below conditions 1 - the measurement per moats per day - MOATs and Data are both available 2 - the measurement (averaged across moats) - group by / summarize tools 3- (data gapped situation- no value matches) take the previous daily average based on observation - lag? previous line on a dataframe ā¦ dplyr tool ā¦ https://dplyr.tidyverse.org/reference/lead-lag.html 4 - āthe winning valueā case_when #corrected conductivity value
8.1a Plots (timeseries verification)
Simple Plot 8.1.1
# 8.1 Plots (timeseries verification)
p <- ggplot(H2Ochemdf, aes(x=ObservationDate, y=salinity))+
geom_line() +
ylim (30.05, 27.00)
p
Simple Plot 8.1.2 Salinity Values all Treatments, All MOATs, During Night Period
ggplot(subset(H2Ochemdf,
period %in% ("night")),
aes(x=ObservationDate, y=salinity)) +
geom_point(aes(colour=period, point=)) +
ggtitle("Salinity Constants all Treatments, All MOATs, During Night Period")
Simple Plot 8.1.3
ggplot(subset(H2Ochemdf,
period %in% ("night")),
aes(x=ObservationDate, y=salinity)) +
geom_point(aes(colour=moats, point=)) +
ggtitle("Salinity Constants all Treatments, All MOATs, During Night Period")
8.1.4 Salinity Constants, MOATs 01, During Night Period
ggplot(subset(H2Ochemdf[H2Ochemdf$moats == "M01", ],
period %in% ("day")),
aes(x=dateTime, y=salinity)) +
geom_point(aes(colour=moats, point=)) +
ggtitle("Salinity Constant, MOATs 01, During Night Period")
8.2 Practical Salinity Units - Measurements Per Moats per Day
These observations consist of salinity measurements taken per MOATS per day. The observation were recorded per MOATS per day as the researcher took point measurements from the aquarium box while doing DO measurements.
8.3 Practical Salinity Units - Daily Avg Measurement
Manual records sometimes have holes Monday through Friday averages were recorded with greater fidelity than weekend averages.
8.4 Practical Salinity Units - Previous Daily Avg Measurement
DO point measurements occured weekly. Salinity averages were typically recorded every day, Monday- Friday. Some DO measurements were a week apart. The previous daily average was thought to be a better reflection than the last per Day, per MOATs salinity measurement.
8.4.1 Last Observation Carried Forward Function
(Description) Generic function for replacing each NA with the most recent non-NA prior to it. This function was used to cover those gaps that occured most weekends.
8.5 Practical Salinity Units - Assumed Salinity Value
Before the start of the study, a salinity of 28.8PSU was observed the week prior to the studyās start
8.6 Practical Salinity Units - Final Salinity Value
cycling between different salinity measurements required moving between character based and numeric based functions.
# 8.6a Review of Vectors
# Review of the three different salinity values
# H2Ochemdf$PSUperMOATs measurement
# PSUperMOATs per MOATS per Day
## PSUperMOATs
# H2Ochemdf$PSUprevObs Daily Average
# PSUavgDaily measurment across MOATs
## PSUavgDaily
# H2Ochemdf$PSUprevObs Previous Daily
# PSUprevObs Average to cover those gaps
## PSUprevObs
# Sailinity Assumption 28.8
# Dates w/o Dates without measurement 23~30SEP20
28.8## [1] 28.8# Final Salinity
H2Ochemdf$Final_PSU <- 0.0
# H2Ochemdf$PSUperMOATs <- ""
# H2Ochemdf$PSUavgDaily <- ""
# H2Ochemdf$PSUprevObs <- ""
# H2Ochemdf$assumed_PSU <- ""
H2Ochemdf$Final_PSU <- as.numeric(case_when(
H2Ochemdf$PSUperMOATs != 'NA' ~ H2Ochemdf$PSUperMOATs,
H2Ochemdf$PSUavgDaily != 'NA' ~ H2Ochemdf$PSUavgDaily,
H2Ochemdf$PSUprevObs != 'NA' ~ H2Ochemdf$PSUprevObs,
TRUE ~ as.character(H2Ochemdf$assumed_PSU),
))8.7A Variation in Salinity Across all Replicates
FinalSalinity.by.Day <- ggplot(H2Ochemdf,
aes(x=ObservationDate, y=Final_PSU))+
geom_point(aes(colour=moats, point=)) +
ggtitle("Salinity Values, all MOATS (Daily Value)")
FinalSalinity.by.Day
8.8 Variation in Salinity- All Change Treatment
plot8.8 <- ggplot(subset(H2Ochemdf,
treatment %in% ("allchange")),
aes(x=ObservationDate, y=Final_PSU)) +
geom_point(aes(colour=moats, point=)) +
ggtitle("Salinity Variation Across All Change Treatment Replicates")
plot8.8
8.9 Variation in Salinity- High Temperature Conditions Treatment
plot8.9 <- ggplot(subset(H2Ochemdf,
treatment %in% ("hightemperature")),
aes(x=ObservationDate, y=Final_PSU)) +
geom_point(aes(colour=moats, point=)) +
ggtitle("Salinity Variation Across High Temperature Conditions Treatment Replicates")
plot8.9
9.) Creating Percent Dissoved Oxy Value
The Library wql is required for the following chunk The package name stands for ``water qualityāā and reflects the # original focus on time series data for physical and chemical # properties of water, as well as the biota. Intended for # programs that sample approximately monthly, quarterly or # annually at discrete stations, a feature of many legacy data # sets. Most of the functions should be useful for analysis of # similar-frequency time series regardless of the subject # matter.
9.1) Creating Percent Dissolved Oxy Value - creating variables
#assumption with the 28.8 standard salinity reading
H2Ochemdf$percentDOassumpt <- ""
H2Ochemdf$percentDOassumpt <- as.numeric(H2Ochemdf$percentDOassumpt)
H2Ochemdf$assumedSatDOmg <- ""
H2Ochemdf$assumedSatDOmg <- as.numeric(H2Ochemdf$assumedSatDOmg)
# the percent DO
H2Ochemdf$percentDO <- ""
H2Ochemdf$percentDO <- as.numeric(H2Ochemdf$percentDO)
# Observed / Measured Salinity readings informed answers
H2Ochemdf$obseveredSatDOmg <- ""
H2Ochemdf$obseveredSatDOmg <- as.numeric(H2Ochemdf$obseveredSatDOmg)
H2Ochemdf$actualDOmg <- ""
H2Ochemdf$actualDOmg <- as.numeric(H2Ochemdf$actualDOmg)
# Review of Values
# H2Ochemdf$percentDOassumpt
# H2Ochemdf$assumedSatDOmg
# H2Ochemdf$percentDO
# H2Ochemdf$obseveredSatDOmg
# H2Ochemdf$actualDOmg9.2) Creating Percent Dissoved Oxy Value - assumed DO saturation variable creation
# saturated mg/L DO at obseved temperature and assumed salinity
# the oxySol() function is form the wql package
H2Ochemdf$assumedSatDOmg <- oxySol(H2Ochemdf$sTemperature,
H2Ochemdf$assumed_PSU)
SatDOplot <- ggplot(H2Ochemdf, aes(x=ObservationDate,
y=assumedSatDOmg))+
geom_point(aes(colour=treatment, point=)) +
ggtitle("Assumed Saturation of Dissolved Oxygen Across all MOATS")
SatDOplot
9.3) Creating Percent Dissoved Oxy Value - Percent DO (with assumptions)
# Back calculated fraction DO as reported by the oxygen sensor
H2Ochemdf$percentDOassumpt <- ""
H2Ochemdf$percentDOassumpt <- as.numeric(H2Ochemdf$percentDOassumpt)
H2Ochemdf$percentDOassumpt <- H2Ochemdf$DO / H2Ochemdf$assumedSatDOmg
p5 <- ggplot(H2Ochemdf, aes(x=dateTime, y=percentDOassumpt)) +
geom_point(aes(colour=period, point=)) +
ggtitle("Assumed Percent DO (All Treatments & All MOATs) Colored by Period")
p5
9.3a Filtering to Display ramp DO activity
f.H2Ochemdf.ramp = filter(H2Ochemdf,
!moats %in% c('M03', "M04", "M05", "M11") &
treatment %in% c("current", "allchange", "hightemperature") &
period != "night")
f.H2Ochemdf.ramp$moats <- droplevels(f.H2Ochemdf.ramp$moats)
f.H2Ochemdf.ramp$treatment <- factor(f.H2Ochemdf.ramp$treatment)
p5 <- ggplot(f.H2Ochemdf.ramp, aes(x=dateTime, y=percentDOassumpt)) +
geom_point(aes(colour=period, point=)) +
facet_wrap(~treatment)
ggtitle("Assumed Percent DO (All Treatments & All MOATs) Ramping Up From Day to Night")## $title
## [1] "Assumed Percent DO (All Treatments & All MOATs) Ramping Up From Day to Night"
##
## attr(,"class")
## [1] "labels"
9.5 Creating Percent Dissoved Oxy Value
#*********************************
## 9.5) Creating Percent Dissoved Oxy Value - Observed Saturated DOmg (measured salinity)
#*********************************
# #satured mg/L at observed temperature and observed (not assumed) salinity
H2Ochemdf$sTemperature <- as.numeric(H2Ochemdf$sTemperature)
H2Ochemdf$obseveredSatDOmg <- oxySol(H2Ochemdf$sTemperature, H2Ochemdf$Final_PSU)9.6 Creating Percent Dissoved Oxy Value - Observed/Actual DO (in mg)
#*********************************
## 9.6) Creating Percent Dissoved Oxy Value - Observed/Actual DO (in mg) Saturated DOmg (measured salinity)
#*********************************
# actual DO mg at observed temperature and salinity
H2Ochemdf$percentDO <- H2Ochemdf$DO / H2Ochemdf$assumedSatDOmg
H2Ochemdf$actualDOmg <- H2Ochemdf$percentDO * H2Ochemdf$obseveredSatDOmg
actualDOmgPLOT <- ggplot(H2Ochemdf, aes(x=dateTime, y=H2Ochemdf$actualDOmg)) +
geom_point(aes(colour=period, point=)) +
ggtitle("Actual DO (mg/L) (All Treatments & All MOATs) Colored by Period")
actualDOmgPLOT## Warning: Use of `H2Ochemdf$actualDOmg` is discouraged. Use `actualDOmg` instead.## Warning: Removed 34 rows containing missing values (geom_point).
10.0) Filtering Data
# # 10.0) Filtering Data
H2Ochemdf <- H2Ochemdf %>% filter(!moats %in% c("M03", "M04", "M05", "M11")) %>%
filter(sTemperature>= 5 & sTemperature<=30) %>%
filter(treatment %in% c("current", "allchange", "hightemperature")) %>%
filter(period != "other")
write.csv(H2Ochemdf, "2020.10.15_H2Ochemdf.csv")10.1 Filtering Data
#*********************************
## 10.1) Filtering Data-
#*********************************
filteredFrame = filter(H2Ochemdf,
!moats %in% c('M03', "M04", "M05", "M11") &
(aTemperature>= 5 & aTemperature<=30) &
treatment %in% c("current", "allchange", "hightemperature") &
period != "other")
d.temp.insitu = filter(d.temp.insitu,
!moats %in% c('M03', "M04", "M05", "M11") &
(aTemperature>= 5 & aTemperature<=30) &
treatment %in% c("current", "allchange", "hightemperature") &
period != "other")
n.temp.insitu = filter(n.temp.insitu,
!moats %in% c('M03', "M04", "M05", "M11") &
(aTemperature>= 5 & aTemperature<=30) &
treatment %in% c("current", "allchange", "hightemperature") &
period != "other")
class(ReferenceTime)## [1] "POSIXct" "POSIXt"#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
## 10.1a Dropping levels and factors
filteredFrame$moats <- droplevels(filteredFrame$moats)
filteredFrame$treatment <- factor(filteredFrame$treatment)
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
##10.1b Summary Table for Aquarium Temperature if further needed for water chemistry statistics
Cdml.daynight.aTemp.summary <- Cdml %>% group_by(treatment, period) %>%
summarize(sd = sd(aTemperature, na.rm = TRUE),
mean = mean(aTemperature, na.rm = TRUE),
median = median(aTemperature, na.rm = TRUE),
IQR = IQR(aTemperature, na.rm = TRUE),
n = n()) %>%
mutate(se = sd/sqrt(n)) %>%
mutate(ci = se*1.96)## `summarise()` regrouping output by 'treatment' (override with `.groups` argument)10.2 Filtering and Modifying Data for insitu measurements
#10.2a Creating values for date and time to be applied for period
dn.DO.insitu$ObservationDate <- ""
dn.DO.insitu$ObservationTime <- ""
dn.DO.insitu$dateTime<- ""
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#10.2b Create dateTime objects
dn.DO.insitu$dateTime <- as.POSIXct(sprintf("%s %s", dn.DO.insitu$Date, dn.DO.insitu$Time), format="%m/%d/%y %H:%M")
dn.DO.insitu$ObservationDate <- as.Date(dn.DO.insitu$dateTime)
dn.DO.insitu$ObservationTime <- format(as.POSIXct(dn.DO.insitu$dateTime) ,format = "%H:%M:%S")
dn.DO.insitu <- filter(dn.DO.insitu,
!MOATS %in% c('3', "4", "5", "11") &
treatment %in% c("current", "allchange", "hightemperature") &
!treatment %in% c("Ambient"))
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#10.2bc Creating period with same argument as appiled to dml/Cdml/ and H2Ochemdf
dn.DO.insitu$period <-""
dn.DO.insitu <- dn.DO.insitu %>% mutate(period=case_when(
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& (ObservationTime >= "12:00:00")
& (ObservationTime <="21:00:00") ~"night",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& (ObservationTime >= "12:31:00")
& (ObservationTime <="21:00:00") ~"night",
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& ((ObservationTime >= "21:01:00")
| (ObservationTime <="11:29:00")) ~"day",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& ((ObservationTime >= "21:01:00")
| (ObservationTime <= "12:01:00")) ~"day",
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& ((ObservationTime >= "11:30:00")
| (ObservationTime <="12:00:00")) ~"D2Nramp",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& ((ObservationTime >= "12:01:00")
| (ObservationTime <= "12:30:00")) ~"D2Nramp",
TRUE ~"other"
)
)
#|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# d.DO.insitu = filter(d.DO.insitu,
# !MOATS %in% c('3', "4", "5", "11") &
# treatment %in% c("current", "allchange", "hightemperature"))
#
# d.DO.insitu$dateTime <- as.POSIXct(sprintf("%s %s", d.DO.insitu$Date, d.DO.insitu$Time), format="%m/%d/%y %H:%M")
#
#
# n.DO.insitu = filter(n.DO.insitu,
# !MOATS %in% c('3', "4", "5", "11") &
# treatment %in% c("current", "allchange", "hightemperature") &
# !treatment %in% c("Ambient"))
#
# n.DO.insitu$dateTime <- as.POSIXct(sprintf("%s %s", n.DO.insitu$Date, n.DO.insitu$Time), format="%m/%d/%y %H:%M")10.2a Bringing Day and Night Datasets Together
# dim(d.DO.insitu)
# dim(n.DO.insitu)
#
# DO.insitu <- merge(d.DO.insitu, n.DO.insitu,by="dateTime")
#
#
# DO.insitu.1 <- merge(d.DO.insitu, n.DO.insitu, by = "dateTime", all = TRUE)
#
# #DO.insitu.2 <- merge(d.DO.insitu, n.DO.insitu, by.x = "dateTime", by.y = "Date").
# #DOinsitu.3 <- rbind(d.DO.insitu, n.DO.insitu)10.2b SPEC insitu data
## [1] 413 24spec.insitu$treatment <- ""
spec.insitu$treatment[spec.insitu$unit_number == "7" | spec.insitu$unit_number== "10" | spec.insitu$unit_number== "12"] <- "current"
spec.insitu$treatment[spec.insitu$unit_number == "1"| spec.insitu$unit_number== "6"] <- "hightemperature"
spec.insitu$treatment[spec.insitu$unit_number == "2"| spec.insitu$unit_number== "8" | spec.insitu$unit_number== "13"] <- "allchange"
spec.insitu$treatment[spec.insitu$unit_number == "3"| spec.insitu$unit_number == "4" | spec.insitu$unit_number == "5" | spec.insitu$unit_number== "11"] <- "broken_and_ambientbroken"
# filtering out broken and ambient MOATs
spec.insitu = filter(spec.insitu,
!unit_number %in% c('3', "4", "5", "11"))
# creating observation Dates and observation time to create period simular to existing system data set
spec.insitu$ObservationDate <- ""
spec.insitu$ObservationTime <- ""
# converting time from a fraction of the day by seconds to regular time
# Extracting Hours
spec.insitu$hours <- ""
spec.insitu$hours <- (round(spec.insitu$HoursFromMID, digits = 0))
# Extracting Minutes
spec.insitu$minutes <- ""
spec.insitu$minutes <- (spec.insitu$HoursFromMID %% 1)*60
spec.insitu$minutes <- (round(spec.insitu$minutes, digits = 0))
# Creating a character string seperated by semicolon for hours and minutes
minChar <- as.character(spec.insitu$minutes)
hrChar <- as.character(spec.insitu$hours)
spec.insitu$timeChar <- ""
spec.insitu$timeChar <- paste(hrChar, minChar, sep=":")
#Creating a date time for SPEC measurements
spec.insitu$dateTime <- ""
spec.insitu$dateTime <- as.POSIXct(paste(spec.insitu$date,
spec.insitu$timeChar), "%m/%d/%y %H:%M", tz="UTC")
spec.insitu$ObservationDate <- as.Date(spec.insitu$dateTime)
spec.insitu$ObservationTime <- format(as.POSIXct(spec.insitu$dateTime) ,format = "%H:%M")
spec.insitu$period <-""
spec.insitu <- spec.insitu %>% mutate(period=case_when(
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& (ObservationTime >= "12:00:00")
& (ObservationTime <="21:00:00") ~"night",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& (ObservationTime >= "12:31:00")
& (ObservationTime <="21:00:00") ~"night",
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& ((ObservationTime >= "21:01:00")
| (ObservationTime <="11:29:00")) ~"day",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& ((ObservationTime >= "21:01:00")
| (ObservationTime <= "12:01:00")) ~"day",
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& ((ObservationTime >= "11:30:00")
| (ObservationTime <="12:00:00")) ~"D2Nramp",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& ((ObservationTime >= "12:01:00")
| (ObservationTime <= "12:30:00")) ~"D2Nramp",
TRUE ~"other"
)
)
spec.insitu <- filter(spec.insitu,
!unit_number %in% c('3', "4", "5", "11") &
treatment %in% c("current", "allchange", "hightemperature") &
period != "other")
# #filteredSPECFrame$unit_number <- droplevels(filteredSPECFrame$unit_number)
# filteredSPECFrame$treatment <- factor(filteredSPECFrame$treatment)11.0) DO averages- days and nights
11.1 DO averages- days and nights- DAY
## 11.1 DO averages- days and nights
# Creating Values
# Day
allchgDay_DO <- subset(H2Ochemdf,
period == "day" & treatment == "allchange",
select = c(dateTime, actualDOmg))
curDay_DO <- subset(H2Ochemdf,
period == "day" & treatment == "current",
select = c(dateTime, actualDOmg))
hitempDay_DO <- subset(H2Ochemdf,
period == "day" & treatment == "hightemperature",
select = c(dateTime, actualDOmg))11.2 DO averages- days and nights- NIGHT
# Creating Values
# Night
allchgNightDO <- subset(H2Ochemdf,
period == "night" & treatment == "allchange",
select = c(dateTime, actualDOmg ))
curNightDO <- subset(H2Ochemdf,
period == "night" & treatment == "current",
select = c(dateTime, actualDOmg ))
hitempNightDO <- subset(H2Ochemdf,
period == "night" & treatment == "hightemperature",
select = c(dateTime, actualDOmg ))12.0) DO Day/Night Summary
12.1 DO Day/Night Summary
## [1] 18273 21H2Ochemdf.daynight.actualDO.summary <- H2Ochemdf %>% group_by(treatment, period) %>%
summarize(sd = sd(actualDOmg, na.rm = TRUE),
mean = mean(actualDOmg, na.rm = TRUE),
median = median(actualDOmg, na.rm = TRUE),
IQR = IQR(actualDOmg, na.rm = TRUE),
n = n(), .groups= "drop_last") %>%
mutate(se = sd/sqrt(n)) %>%
mutate(ci = se*1.96)
write.csv(H2Ochemdf.daynight.actualDO.summary, "2020.10.15_H2Ochemdf_daynight_actualDO_summary.csv")12.2 pH Day/Night Summary
## [1] 18273 21H2Ochemdf.daynight.pH.summary <- H2Ochemdf %>% group_by(treatment, period) %>%
summarize(sd = sd(pH, na.rm = TRUE),
mean = mean(pH, na.rm = TRUE),
median = median(pH, na.rm = TRUE),
IQR = IQR(pH, na.rm = TRUE),
n = n(), .groups= "drop_last") %>%
mutate(se = sd/sqrt(n)) %>%
mutate(ci = se*1.96)
write.csv(H2Ochemdf.daynight.pH.summary, "2020.10.15_H2Ochemdf_daynight_pH_summary.csv")12.3 DO Water Chemistry PlotsSummary Table Plots
plerts <- ggplot(H2Ochemdf, aes(treatment, actualDOmg)) +
geom_jitter(color = "grey") +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = H2Ochemdf.daynight.actualDO.summary, aes(x=treatment, y=mean), size=5, color = "purple") +
geom_errorbar(data =H2Ochemdf.daynight.actualDO.summary,
aes(x=treatment, y=mean, ymin = mean-sd, ymax = mean+sd),
color = "blue") +
geom_errorbar(data = H2Ochemdf.daynight.actualDO.summary,
aes(x=treatment, y=mean, ymin = mean-ci, ymax = mean+ci),
colour = "red") +
facet_wrap(~period) +
geom_jitter(data = dn.DO.insitu, aes(treatment, actualDOmg),
color = "black") +
facet_wrap(~period) +
ggtitle("All Treatments, Actual Dissolved Oxygen (mg) Across Periods") +
theme_bw()
plerts## Warning: Removed 3 rows containing missing values (geom_point).
13.1 pH Water Chemistry Plots (Summary Table Plots)
ggplot(H2Ochemdf, aes(treatment, pH)) +
geom_jitter(color = "grey") +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = H2Ochemdf.daynight.pH.summary, aes(x=treatment, y=mean), size=5, color = "purple") +
geom_errorbar(data =H2Ochemdf.daynight.pH.summary,
aes(x=treatment, y=mean, ymin = mean-sd, ymax = mean+sd),
color = "blue") +
geom_errorbar(data = H2Ochemdf.daynight.pH.summary,
aes(x=treatment, y=mean, ymin = mean-ci, ymax = mean+ci),
colour = "red") +
geom_jitter(data = spec.insitu, aes(treatment, pH),
color = "black") +
facet_wrap(~period) +
ggtitle("All Treatments, Water Chemistry pH, Across Periods") +
theme_bw()## Warning: Removed 10 rows containing missing values (geom_point).
13.1a SPEC data Summarized
## [1] 258 32pH.insitu.daynight.pH.summary <- spec.insitu %>% group_by(treatment, period) %>%
summarize(sd = sd(pH, na.rm = TRUE),
mean = mean(pH, na.rm = TRUE),
median = median(pH, na.rm = TRUE),
IQR = IQR(pH, na.rm = TRUE),
n = n(), .groups= "drop_last") %>%
mutate(se = sd/sqrt(n)) %>%
mutate(ci = se*1.96)
write.csv(pH.insitu.daynight.pH.summary, "2020.10.26_pH.insitu.daynight.pH.summary.csv")13.1b Exploring All Change Day Period
lack of clear day and night might be due to how much change is happening durring the ramp period
ggplot(subset(H2Ochemdf[H2Ochemdf$treatment == "allchange", ],
period %in% ("day")),
aes(x=dateTime, y=pH)) +
geom_point(aes(colour=moats, point=)) +
# geom_violin() +
ylim (6.6, 7.8) 
14.0 Water Chemistry Around Treatment Means: Histogram Evolution to ECDF plots
The following graphs show dissolved oxygen and pH observations. The goal is to describe the time the system spent at different set points. The empirical cumulative distribution function (ECDF) provides an alternative visualisation of distribution. Compared to other visualizations that rely on density (like geom_histogram()), the ECDF doesnāt require any tuning parameters and handles both continuous and categorical variables.
## 14.1 Histogram plot- all moats, all treatments, all periods
# Cdml %>% ggplot(data=.)+geom_histogram(aes(x=aTemperature), binwidth = .05) +
# xlim(5,30) +
# ylim(0, 10000)# 14.2a Histrogram Scaled by Time
Section below walks how to quantify each ābinā by the observation period = 6.8minutes
#find duration of each observations in hours
durObsHours <- 6.8/60
# the count (number of observations in each bin) is scale by time duration of each obs
# option to also include polygon
# could fix the vline to only show the value relevant for each plot
#vline could show acutal mean temperature, not just target
# each bin shows 0.2 degrees
plot13.a_temp <- ggplot(Cdml, aes(aTemperature)) +
geom_histogram(aes(y=..count..*durObsHours),
binwidth = 0.2) +
# geom_freqpoly(aes(y=..count..*durObs)) +
geom_vline(xintercept = c(11,12,13,14), colour = "red") +
facet_wrap(vars(treatment, period), ncol = 2, scales = "fixed") +
theme_bw()
plot13.a_temp
14.2b Histrogram Scaled by Time Graphs plus Y liminator
## Warning: Removed 26 rows containing missing values (geom_bar).
14.2c Histrogram Scaled by Time Graphs faceted wrapped
#find duration of each observations in hours
durObsHours <- 6.8/60
# the count (number of observations in each bin) is scale by time duration of each obs
# option to also include polygon
# could fix the vline to only show the value relevant for each plot
#vline could show acutal mean temperature, not just target
# each bin shows 0.2 degrees
plot13.2c_DO <- ggplot(H2Ochemdf, aes(actualDOmg)) +
geom_histogram(aes(y=..count..*durObsHours),
binwidth = 0.2) +
# geom_freqpoly(aes(y=..count..*durObs)) +
geom_vline(xintercept = c(4,6,8,10), colour = "red") +
facet_wrap(vars(treatment, period), ncol = 2, scales = "fixed") +
theme_bw()
plot13.2c_DO
14.3 Cumulative Frequency Distribution
ECFDplot <- ggplot(H2Ochemdf, aes(actualDOmg)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = c(4,6,8,10), colour = "red") +
facet_wrap(vars(treatment, period), ncol = 2, scales = "fixed") +
theme_bw()
ECFDplot 
14.3b Cumulative Frequency Distribution, facet wrap, geomlines by DO percent
ECDFplot2 <- ggplot(H2Ochemdf, aes(percentDO)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = c(.6,.7,.8,.9), colour = "red") +
facet_wrap(vars(treatment, period), ncol = 2, scales = "fixed") +
theme_bw()
ECDFplot2
14.3c Cumulative Frequency Distribution MOATs grouped by Treatment Colored by MOATs
ECDFplot13.3c <- ggplot(H2Ochemdf, aes(actualDOmg)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = H2Ochemdf.daynight.actualDO.summary$mean, colour = "red") +
facet_wrap(vars(treatment, period), ncol = 2, scales = "fixed") +
theme_bw()
ECDFplot13.3c
14.3d Actual DOmg ECDF Plot with Summary Table
## 14.3d ECDF Plot
ECDF_Plot_13.3d <- ggplot(H2Ochemdf, aes(actualDOmg)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = H2Ochemdf.daynight.actualDO.summary$mean, colour = "red") +
facet_wrap(vars(treatment, period), ncol = 2, scales = "fixed") +
theme_bw()
ECDF_Plot_13.3d
13.4 Creating the Function(s) coolECDF suite
The cool ECDF function should incorporate the summary statistics and displacy averages coolECDF- incorporates aquarium temperature summary statisics DOcoolECDF- will incorporate actual mg of dissolved oxygen summary statistics pHcoolECDF- will incorporate pH summary statistics. pH measurements discussed more in chapter 13.
## 13.4 Creating a function to incorporate our statistics- displaying the averages
#*********************************
## Aquarium Temperature Summary Statistics Function
#*********************************
coolECDF <- function(d, dsum, treat, per){
dsum <- dsum %>% filter(treatment == treat & period == per )
p <- d %>% filter(treatment == treat & period == per ) %>% ggplot(aes(aTemperature)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = dsum$mean, colour = "red") +
ggtitle(paste(treat, per)) +
theme_bw()
return(p)
}
#*********************************
## Actual DOmg Summary Statistics Function
#*********************************
DOcoolECDF <- function(d, dsum, treat, per){
dsum <- dsum %>% filter(treatment == treat & period == per )
p <- d %>% filter(treatment == treat & period == per ) %>% ggplot(aes(actualDOmg)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = dsum$mean, colour = "red") +
ggtitle(paste(treat, per)) +
theme_bw()
return(p)
}
#*********************************
## pH Summary Statistics Function
#*********************************
pHcoolECDF <- function(d, dsum, treat, per){
dsum <- dsum %>% filter(treatment == treat & period == per )
p <- d %>% filter(treatment == treat & period == per ) %>% ggplot(aes(pH)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = dsum$mean, colour = "red") +
ggtitle(paste(treat, per)) +
theme_bw()
return(p)
}14.4b Aquarium Temperature with Empirical Cumulative Distribution āCoolā function (ECDF)
## 14.4a Using the coolECDF function per treatment & period
cfd.a.d <- coolECDF(Cdml, Cdml.daynight.aTemp.summary, "allchange", "day")
cfd.a.n <- coolECDF(Cdml, Cdml.daynight.aTemp.summary, "allchange", "night")
cfd.c.d <- coolECDF(Cdml, Cdml.daynight.aTemp.summary, "current", "day")
cfd.c.n <- coolECDF(Cdml, Cdml.daynight.aTemp.summary, "current", "night")
cfd.h.d <- coolECDF(Cdml, Cdml.daynight.aTemp.summary, "hightemperature", "day")
cfd.h.n <- coolECDF(Cdml, Cdml.daynight.aTemp.summary, "hightemperature", "night")
## 14.4b Incorporating Grid Arrange to display the ECDF plots
# Basic Grid Arrange
grid.arrange(cfd.a.d, cfd.c.d, cfd.h.d, nrow = 1)
#make a list of all the plots then pass the list to grid.arrange()
ecdfList <- list(cfd.a.d, cfd.c.d, cfd.h.d, cfd.a.n, cfd.c.n, cfd.h.n)
grid.arrange(grobs = ecdfList, ncol=3) 
14.5 Actual Milligrams of Dissolved Oxygen with Empirical Cumulative Distribution āCoolā function (ECDF)
#*********************************
## DO & COOL ECDF function
#*********************************
## 13.5a Using the coolECDF function per treatment & period for DOmg
# DO.cfd.allchg.d <- DOcoolECDF(H2Ochemdf, H2Ochemdf.daynight.actualDO.summary, "allchange", "day",)
# DO.cfd.allchg.r <- DOcoolECDF(H2Ochemdf, H2Ochemdf.daynight.actualDO.summary, "allchange", "D2Nramp",)
# DO.cfd.allchg.n <- DOcoolECDF(H2Ochemdf, H2Ochemdf.daynight.actualDO.summary, "allchange", "night")
#
#
# DO.cfd.cur.d <- DOcoolECDF(H2Ochemdf, H2Ochemdf.daynight.actualDO.summary, "current", "day")
# DO.cfd.cur.r <- DOcoolECDF(H2Ochemdf, H2Ochemdf.daynight.actualDO.summary, "current", "D2Nramp")
# DO.cfd.cur.n <- DOcoolECDF(H2Ochemdf, H2Ochemdf.daynight.actualDO.summary, "current", "night")
#
#
# DO.cfd.hi.d <- DOcoolECDF(H2Ochemdf, H2Ochemdf.daynight.actualDO.summary, "hightemperature", "day")
# DO.cfd.hi.r <- DOcoolECDF(H2Ochemdf, H2Ochemdf.daynight.actualDO.summary, "hightemperature", "D2Nramp")
# DO.cfd.hi.n <- DOcoolECDF(H2Ochemdf, H2Ochemdf.daynight.actualDO.summary, "hightemperature", "night")
#
#
# ## 14.4b Incorporating Grid Arrange to display the ECDF plots
# # Basic Grid Arrange
# # Note extra period - the ramp period
#
# grid.arrange(DO.cfd.allchg.d , DO.cfd.cur.d, DO.cfd.hi.d, nrow = 1)
#
# grid.arrange(DO.cfd.allchg.r , DO.cfd.cur.r, DO.cfd.hi.r, nrow = 1)
#
# grid.arrange(DO.cfd.allchg.n , DO.cfd.cur.n, DO.cfd.hi.n, nrow = 1)
#
#
# #make a list of all the plots then pass the list to grid.arrange()
# ecdfList <- list(DO.cfd.allchg.d,
# DO.cfd.allchg.r,
# DO.cfd.allchg.n,
#
# DO.cfd.cur.d,
# DO.cfd.cur.r,
# DO.cfd.cur.n,
#
# DO.cfd.hi.d,
# DO.cfd.hi.r,
# DO.cfd.hi.n
# )
# grid.arrange(grobs = ecdfList, ncol=3)
# 14.6 pH Water Chemistry with Empirical Cumulative Distribution āCoolā function (ECDF)
#*********************************
## pH Water Chemistry & COOL ECDF function
#*********************************
## 14.6a Using the coolECDF function per treatment & period for pH
# pH.cfd.allchg.d <- pHcoolECDF(H2Ochemdf, H2Ochemdf.daynight.pH.summary, "allchange", "day",)
# pH.cfd.allchg.r <- pHcoolECDF(H2Ochemdf, H2Ochemdf.daynight.pH.summary, "allchange", "D2Nramp",)
# pH.cfd.allchg.n <- pHcoolECDF(H2Ochemdf, H2Ochemdf.daynight.pH.summary, "allchange", "night")
#
#
# pH.cfd.cur.d <- pHcoolECDF(H2Ochemdf, H2Ochemdf.daynight.pH.summary, "current", "day")
# pH.cfd.cur.r <- pHcoolECDF(H2Ochemdf, H2Ochemdf.daynight.pH.summary, "current", "D2Nramp")
# pH.cfd.cur.n <- pHcoolECDF(H2Ochemdf, H2Ochemdf.daynight.pH.summary, "current", "night")
#
#
# pH.cfd.hi.d <- pHcoolECDF(H2Ochemdf, H2Ochemdf.daynight.pH.summary, "hightemperature", "day")
# pH.cfd.hi.r <- pHcoolECDF(H2Ochemdf, H2Ochemdf.daynight.pH.summary, "hightemperature", "D2Nramp")
# pH.cfd.hi.n <- pHcoolECDF(H2Ochemdf, H2Ochemdf.daynight.pH.summary, "hightemperature", "night")
#
#
# ## 14.4b Incorporating Grid Arrange to display the ECDF plots
# # Basic Grid Arrange
# # Note extra period - the ramp period
#
# grid.arrange(pH.cfd.allchg.d , pH.cfd.cur.d, pH.cfd.hi.d, nrow = 1)
#
# grid.arrange(pH.cfd.allchg.r , pH.cfd.cur.r, pH.cfd.hi.r, nrow = 1)
#
# grid.arrange(pH.cfd.allchg.n , pH.cfd.cur.n, pH.cfd.hi.n, nrow = 1)
#
#
# #make a list of all the plots then pass the list to grid.arrange()
# ecdfList <- list(pH.cfd.allchg.d,
# pH.cfd.allchg.r,
# pH.cfd.allchg.n,
#
# pH.cfd.cur.d,
# pH.cfd.cur.r,
# pH.cfd.cur.n,
#
# pH.cfd.hi.d,
# pH.cfd.hi.r,
# pH.cfd.hi.n
# )
# grid.arrange(grobs = ecdfList, ncol=3)
#
# A. Executive Summary
ExecutiveSummary
A.1 Creating the Dataframe
Creating the Water Chemistry Dataframe. Differences between dataframes outlined in the graphic below:
A.2 Decisions About Salinity
A mosaic of salinity values were used to update the salinity constants for correct DO measurements. Decision needed to determine if enough variation present to continue with āFinal_PSUā or return to using the salinity constants
FinalSalinity.Daily <- ggplot(H2Ochemdf,
aes(x=ObservationDate, y=Final_PSU))+
geom_point(aes(colour=moats, point=)) +
ggtitle("Salinity Values, all MOATS (Daily Value)")
FinalSalinity.Daily
The following plots are broken down by treatment to gain more clarity about observed salinity values. ##### A.2.1 Variation in Salinity- All Change Treatment
plotA.2.1 <- ggplot(subset(H2Ochemdf,
treatment %in% ("allchange")),
aes(x=ObservationDate, y=Final_PSU)) +
geom_point(aes(colour=moats, point=)) +
ggtitle("Salinity Variation Across All Change Treatment Replicates")
plotA.2.1
A.2.2 Variation in Salinity- High Temperature Conditions Treatment
plotA.2.2 <- ggplot(subset(H2Ochemdf,
treatment %in% ("hightemperature")),
aes(x=ObservationDate, y=Final_PSU)) +
geom_point(aes(colour=moats, point=)) +
ggtitle("Salinity Variation Across High Temperature Conditions Treatment Replicates")
plotA.2.2
A.3 āRampā is an added time period
When discussing water chemistry the greatest amount of change would occur during the ramp period. I didnāt want this to affect the systemās summary average for DO or pH, Day and Night respectively. Plot below shows the progression of DO from low Day time set points towards the 100% DO saturation.
A.3a Displaying ramp DO activity
A3a <- ggplot(f.H2Ochemdf.ramp, aes(x=dateTime, y=percentDOassumpt)) +
geom_point(aes(colour=period, point=)) +
facet_wrap(~treatment)
ggtitle("Assumed Percent DO (All Treatments & All MOATs) Ramping Up From Day to Night")## $title
## [1] "Assumed Percent DO (All Treatments & All MOATs) Ramping Up From Day to Night"
##
## attr(,"class")
## [1] "labels"
A.4 Review of How Data were Filtered
Filtering data focused around throwing out incongruous temperatures and dropping faulty MOATs.
A.4.1 Filtering Data
#*********************************
## Filtering Data- Temp
#*********************************
# filteredFrame = filter(H2Ochemdf,
# !moats %in% c('M03', "M04", "M05", "M11") &
# (aTemperature>= 5 & aTemperature<=30) &
# treatment %in% c("current", "allchange", "hightemperature") &
# period != "other")
#
# d.temp.insitu = filter(d.temp.insitu,
# !moats %in% c('M03', "M04", "M05", "M11") &
# (aTemperature>= 5 & aTemperature<=30) &
# treatment %in% c("current", "allchange", "hightemperature") &
# period != "other")
#
# n.temp.insitu = filter(n.temp.insitu,
# !moats %in% c('M03', "M04", "M05", "M11") &
# (aTemperature>= 5 & aTemperature<=30) &
# treatment %in% c("current", "allchange", "hightemperature") &
# period != "other")
#
# class(ReferenceTime)
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# #*********************************
# ## Filtering Data- DO
# #*********************************
#
# #10.2a Creating values for date and time to be applied for period
# dn.DO.insitu$ObservationDate <- ""
# dn.DO.insitu$ObservationTime <- ""
# dn.DO.insitu$dateTime<- ""
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# #10.2b Create dateTime objects
# dn.DO.insitu$dateTime <- as.POSIXct(sprintf("%s %s", dn.DO.insitu$Date, dn.DO.insitu$Time), format="%m/%d/%y %H:%M")
#
# dn.DO.insitu$ObservationDate <- as.Date(dn.DO.insitu$dateTime)
#
# dn.DO.insitu$ObservationTime <- format(as.POSIXct(dn.DO.insitu$dateTime) ,format = "%H:%M:%S")
#
# dn.DO.insitu <- filter(dn.DO.insitu,
# !MOATS %in% c('3', "4", "5", "11") &
# treatment %in% c("current", "allchange", "hightemperature") &
# !treatment %in% c("Ambient"))
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# # #10.2bc Creating period with same argument as appiled to dml/Cdml/ and H2Ochemdf
# # dn.DO.insitu$period <-""
# #
# # dn.DO.insitu <- dn.DO.insitu %>% mutate(period=case_when(
# # (ObservationDate >= "2019-09-23")
# # & (ObservationDate <="2019-10-05")
# # & (ObservationTime >= "12:00:00")
# # & (ObservationTime <="21:00:00") ~"night",
# #
# # (ObservationDate >= "2019-10-05")
# # & (ObservationDate <= "2019-10-30")
# # & (ObservationTime >= "12:31:00")
# # & (ObservationTime <="21:00:00") ~"night",
# #
# # (ObservationDate >= "2019-09-23")
# # & (ObservationDate <="2019-10-05")
# # & ((ObservationTime >= "21:01:00")
# # | (ObservationTime <="11:29:00")) ~"day",
# #
# # (ObservationDate >= "2019-10-05")
# # & (ObservationDate <= "2019-10-30")
# # & ((ObservationTime >= "21:01:00")
# # | (ObservationTime <= "12:01:00")) ~"day",
# #
# # (ObservationDate >= "2019-09-23")
# # & (ObservationDate <="2019-10-05")
# # & ((ObservationTime >= "11:30:00")
# # | (ObservationTime <="12:00:00")) ~"D2Nramp",
# #
# # (ObservationDate >= "2019-10-05")
# # & (ObservationDate <= "2019-10-30")
# # & ((ObservationTime >= "12:01:00")
# # | (ObservationTime <= "12:30:00")) ~"D2Nramp",
# #
# #
# # TRUE ~"other"
# # )
# #
# # ) |
# #
# # #*********************************
# # ## Filtering Data- pH
# # #*********************************
# #
# # dim(spec.insitu)
#
# spec.insitu$treatment <- ""
# spec.insitu$treatment[spec.insitu$unit_number == "7" | spec.insitu$unit_number== "10" | spec.insitu$unit_number== "12"] <- "current"
# spec.insitu$treatment[spec.insitu$unit_number == "1"| spec.insitu$unit_number== "6"] <- "hightemperature"
# spec.insitu$treatment[spec.insitu$unit_number == "2"| spec.insitu$unit_number== "8" | spec.insitu$unit_number== "13"] <- "allchange"
# spec.insitu$treatment[spec.insitu$unit_number == "3"| spec.insitu$unit_number == "4" | spec.insitu$unit_number == "5" | spec.insitu$unit_number== "11"] <- "broken_and_ambientbroken"
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# # filtering out broken and ambient MOATs
# spec.insitu = filter(spec.insitu,
# !unit_number %in% c('3', "4", "5", "11"))
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# # creating observation Dates and observation time to create period simular to existing system data set
# spec.insitu$ObservationDate <- ""
# spec.insitu$ObservationTime <- ""
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
# # converting time from a fraction of the day by seconds to regular time
# # Extracting Hours
# spec.insitu$hours <- ""
# spec.insitu$hours <- (round(spec.insitu$HoursFromMID, digits = 0))
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# # Extracting Minutes
# spec.insitu$minutes <- ""
# spec.insitu$minutes <- (spec.insitu$HoursFromMID %% 1)*60
# spec.insitu$minutes <- (round(spec.insitu$minutes, digits = 0))
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# # Creating a character string seperated by semicolon for hours and minutes
# minChar <- as.character(spec.insitu$minutes)
# hrChar <- as.character(spec.insitu$hours)
# spec.insitu$timeChar <- ""
# spec.insitu$timeChar <- paste(hrChar, minChar, sep=":")
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# #Creating a date time for SPEC measurements
# spec.insitu$dateTime <- ""
# spec.insitu$dateTime <- as.POSIXct(paste(spec.insitu$date,
# spec.insitu$timeChar), "%m/%d/%y %H:%M", tz="UTC")
# spec.insitu$ObservationDate <- as.Date(spec.insitu$dateTime)
# spec.insitu$ObservationTime <- format(as.POSIXct(spec.insitu$dateTime) ,format = "%H:%M")
#
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# # Applying standard period interval argument
# spec.insitu$period <-""
#
# spec.insitu <- spec.insitu %>% mutate(period=case_when(
# (ObservationDate >= "2019-09-23")
# & (ObservationDate <="2019-10-05")
# & (ObservationTime >= "12:00:00")
# & (ObservationTime <="21:00:00") ~"night",
#
# (ObservationDate >= "2019-10-05")
# & (ObservationDate <= "2019-10-30")
# & (ObservationTime >= "12:31:00")
# & (ObservationTime <="21:00:00") ~"night",
#
# (ObservationDate >= "2019-09-23")
# & (ObservationDate <="2019-10-05")
# & ((ObservationTime >= "21:01:00")
# | (ObservationTime <="11:29:00")) ~"day",
#
# (ObservationDate >= "2019-10-05")
# & (ObservationDate <= "2019-10-30")
# & ((ObservationTime >= "21:01:00")
# | (ObservationTime <= "12:01:00")) ~"day",
#
# (ObservationDate >= "2019-09-23")
# & (ObservationDate <="2019-10-05")
# & ((ObservationTime >= "11:30:00")
# | (ObservationTime <="12:00:00")) ~"D2Nramp",
#
# (ObservationDate >= "2019-10-05")
# & (ObservationDate <= "2019-10-30")
# & ((ObservationTime >= "12:01:00")
# | (ObservationTime <= "12:30:00")) ~"D2Nramp",
#
#
# TRUE ~"other"
# )
#
# )
#
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
#
#
# spec.insitu <- filter(spec.insitu,
# !unit_number %in% c('3', "4", "5", "11") &
# treatment %in% c("current", "allchange", "hightemperature") &
# period != "other")
#
# #|- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - | A.5.1 Summary Tables with boxplots
The following chunks will show the summary tablesā generation and information plotted with boxplots Order: 1. Aquarium Temperatures 2. Dissolved Oxygen 3. pH
A.5.1 Summary Tables- Aquarium Temperature
##Summary Table for Aquarium Temperature if further needed for water chemistry statistics
Cdml.daynight.aTemp.summary <- Cdml %>% group_by(treatment, period) %>%
summarize(sd = sd(aTemperature, na.rm = TRUE),
mean = mean(aTemperature, na.rm = TRUE),
median = median(aTemperature, na.rm = TRUE),
IQR = IQR(aTemperature, na.rm = TRUE),
n = n()) %>%
mutate(se = sd/sqrt(n)) %>%
mutate(ci = se*1.96)## `summarise()` regrouping output by 'treatment' (override with `.groups` argument)write.csv(Cdml.daynight.aTemp.summary, "2020.10.15_Cdml_daynight_aTemp_summary.csv")
kable(Cdml.daynight.aTemp.summary, digits = 2)| treatment | period | sd | mean | median | IQR | n | se | ci |
|---|---|---|---|---|---|---|---|---|
| allchange | D2Nramp | 0.32 | 13.63 | 13.70 | 0.40 | 166 | 0.02 | 0.05 |
| allchange | day | 0.36 | 12.95 | 12.89 | 0.23 | 4802 | 0.01 | 0.01 |
| allchange | night | 0.25 | 13.90 | 13.92 | 0.19 | 2842 | 0.00 | 0.01 |
| current | D2Nramp | 0.43 | 11.76 | 11.96 | 0.54 | 135 | 0.04 | 0.07 |
| current | day | 0.32 | 11.08 | 11.07 | 0.20 | 3849 | 0.01 | 0.01 |
| current | night | 0.30 | 11.95 | 12.05 | 0.12 | 2285 | 0.01 | 0.01 |
| hightemperature | D2Nramp | 0.48 | 13.45 | 13.49 | 0.57 | 86 | 0.05 | 0.10 |
| hightemperature | day | 0.35 | 12.91 | 12.91 | 0.29 | 2607 | 0.01 | 0.01 |
| hightemperature | night | 0.31 | 13.80 | 13.88 | 0.19 | 1501 | 0.01 | 0.02 |
A.5.2 Summary Tables- Aquarium Temperature- aTemperature Plot with Summary Table Data
ggplot(Cdml, aes(treatment, aTemperature)) +
geom_jitter(color = "grey") +
# geom_jitter(data = d.temp.insitu, aes(treatment, aTemperature)) +
# geom_jitter(data = n.temp.insitu, aes(treatment, aTemperature)) +
# geom_jitter(aes(colour = black)) +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = Cdml.daynight.aTemp.summary, aes(x=treatment, y=mean), size=5, color = "purple") +
geom_errorbar(data = Cdml.daynight.aTemp.summary,
aes(x=treatment, y=mean, ymin = mean-sd, ymax = mean+sd),
color = "blue") +
geom_errorbar(data = Cdml.daynight.aTemp.summary,
aes(x=treatment, y=mean, ymin = mean-ci, ymax = mean+ci),
colour = "red") +
facet_wrap(~period) +
ggtitle("All Treatments, Aquarium Temperature Summarized") +
theme_bw() 
A.5.3 Summary Tables-DO Day/Night Summary
## [1] 18273 21H2Ochemdf.daynight.actualDO.summary <- H2Ochemdf %>% group_by(treatment, period) %>%
summarize(sd = sd(actualDOmg, na.rm = TRUE),
mean = mean(actualDOmg, na.rm = TRUE),
median = median(actualDOmg, na.rm = TRUE),
IQR = IQR(actualDOmg, na.rm = TRUE),
n = n(), .groups= "drop_last") %>%
mutate(se = sd/sqrt(n)) %>%
mutate(ci = se*1.96)
write.csv(H2Ochemdf.daynight.actualDO.summary, "2020.10.26_H2Ochemdf_daynight_actualDO_summary.csv")
kable(H2Ochemdf.daynight.actualDO.summary, digits=2)| treatment | period | sd | mean | median | IQR | n | se | ci |
|---|---|---|---|---|---|---|---|---|
| allchange | D2Nramp | 0.70 | 7.55 | 7.62 | 0.90 | 166 | 0.05 | 0.11 |
| allchange | day | 0.90 | 5.01 | 4.71 | 0.82 | 4802 | 0.01 | 0.03 |
| allchange | night | 0.36 | 8.37 | 8.45 | 0.25 | 2842 | 0.01 | 0.01 |
| current | D2Nramp | 0.56 | 8.33 | 8.42 | 0.90 | 135 | 0.05 | 0.09 |
| current | day | 1.06 | 6.08 | 5.67 | 0.90 | 3849 | 0.02 | 0.03 |
| current | night | 0.44 | 8.80 | 8.92 | 0.26 | 2285 | 0.01 | 0.02 |
| hightemperature | D2Nramp | 0.50 | 8.19 | 8.28 | 0.70 | 86 | 0.05 | 0.11 |
| hightemperature | day | 0.82 | 5.66 | 5.51 | 0.62 | 2607 | 0.02 | 0.03 |
| hightemperature | night | 0.20 | 8.58 | 8.59 | 0.20 | 1501 | 0.01 | 0.01 |
A.5.3 Summary Tables-DO Day/Night Summary - Summary Table Plots
ggplot(H2Ochemdf, aes(treatment, actualDOmg)) +
geom_jitter(color = "grey") +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = H2Ochemdf.daynight.actualDO.summary, aes(x=treatment, y=mean), size=5, color = "purple") +
geom_errorbar(data =H2Ochemdf.daynight.actualDO.summary,
aes(x=treatment, y=mean, ymin = mean-sd, ymax = mean+sd),
color = "blue") +
geom_errorbar(data = H2Ochemdf.daynight.actualDO.summary,
aes(x=treatment, y=mean, ymin = mean-ci, ymax = mean+ci),
colour = "red") +
facet_wrap(~period) +
geom_jitter(data = dn.DO.insitu, aes(treatment, actualDOmg),
color = "black") +
ggtitle("All Treatments, Actual Dissolved Oxygen (mg) Across Periods") +
theme_bw()## Warning: Removed 3 rows containing missing values (geom_point).
A.5.4 Summary Tables-pH Day/Night Summary
## [1] 18273 21H2Ochemdf.daynight.pH.summary <- H2Ochemdf %>% group_by(treatment, period) %>%
summarize(sd = sd(pH, na.rm = TRUE),
mean = mean(pH, na.rm = TRUE),
median = median(pH, na.rm = TRUE),
IQR = IQR(pH, na.rm = TRUE),
n = n(), .groups= "drop_last") %>%
mutate(se = sd/sqrt(n)) %>%
mutate(ci = se*1.96)
write.csv(H2Ochemdf.daynight.pH.summary, "2020.10.26_H2Ochemdf_daynight_pH_summary.csv")
kable(H2Ochemdf.daynight.pH.summary, digits=2)| treatment | period | sd | mean | median | IQR | n | se | ci |
|---|---|---|---|---|---|---|---|---|
| allchange | D2Nramp | 0.02 | 7.57 | 7.57 | 0.03 | 166 | 0 | 0.00 |
| allchange | day | 0.03 | 7.53 | 7.53 | 0.03 | 4802 | 0 | 0.00 |
| allchange | night | 0.02 | 7.56 | 7.56 | 0.03 | 2842 | 0 | 0.00 |
| current | D2Nramp | 0.03 | 7.71 | 7.72 | 0.03 | 135 | 0 | 0.01 |
| current | day | 0.04 | 7.66 | 7.65 | 0.03 | 3849 | 0 | 0.00 |
| current | night | 0.04 | 7.70 | 7.72 | 0.03 | 2285 | 0 | 0.00 |
| hightemperature | D2Nramp | 0.02 | 7.72 | 7.72 | 0.02 | 86 | 0 | 0.00 |
| hightemperature | day | 0.03 | 7.66 | 7.66 | 0.03 | 2607 | 0 | 0.00 |
| hightemperature | night | 0.02 | 7.72 | 7.73 | 0.02 | 1501 | 0 | 0.00 |
A.5.4a Summary Tables-pH Day/Night Summary (Summary Table Plots)
ggplot(H2Ochemdf, aes(treatment, pH)) +
geom_jitter(color = "grey") +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = H2Ochemdf.daynight.pH.summary, aes(x=treatment, y=mean), size=5, color = "purple") +
geom_errorbar(data =H2Ochemdf.daynight.pH.summary,
aes(x=treatment, y=mean, ymin = mean-sd, ymax = mean+sd),
color = "blue") +
geom_errorbar(data = H2Ochemdf.daynight.pH.summary,
aes(x=treatment, y=mean, ymin = mean-ci, ymax = mean+ci),
colour = "red") +
geom_jitter(data = spec.insitu, aes(treatment, pH),
color = "black") +
facet_wrap(~period) +
ggtitle("All Treatments, Water Chemistry pH, Across Periods") +
theme_bw()## Warning: Removed 10 rows containing missing values (geom_point).
.**************END*************# .********************************* .# END OF SCRIPT | END OF DOCUMENT .*********************************
. ___________________8888, . ____________________Y8888b, . ___________________,oA8888888b, . _____________,aaad8888888888888888bo, . __________,d888888888888888888888888888b, . ________,888888888888888888888888888888888b, . _______d8888888888888888888888888888888888888, . ______d888888888888888888888888888888888888888b . _____d888888Pā Y888888888888, . _____88888P' Ybaaaa8888888888l . ___a8888'Y8888Pā V888888 . __d8888888aY8888 . AY/āā \Y8b ``Y8b . Y'YP ~~